【摘要】 现有的通信基站流量调节方法多基于单向神经网络预测调配，边缘信息的缺失导致精度不高。为解决此问题，提出一种基于双向门控循环单元的通信基站流量智能化预测方法。该方法选择门控循环单元，有效捕获时间序列的潜在规律，并突破单向调配方法的缺点，从前后两个方向独立进行训练，利用完整历史信息，实现更精准的预测效果。根据北京国测星绘采集的约8GB的小区上行流量数据，使用单、双向长短期记忆网络以及单向门控循环单元进行比较实验，结果表明，双向门控循环单元方法较对照算法在R~2上平均提高约0.1，预测精度也有显著提升，对基站流量调节起到决策支持作用，具有一定现实意义。更多还原

【Abstract】 The existing methods of regulating traffic are primarily based on unidirectional neural network prediction allocation,and the lack of edge information leads to low accuracy,therefore propose an intelligent traffic prediction method of communication base station based on bi-directional gate recurrent unit to solve this problem. This method selects gate recurrent unit to capture the potential law of the time series effectively,break through the shortcomings of the unidirectional allocation method,and train independently from the front and rear directions,to achieve a more accurate prediction effect with complete historical information. According to the 8GB cell uplink traffic data collected by Beijing guoce satellite mapping,the experiments are compared with long short-term memory,bi-directional long short-term memory and gate recurrent unit. The results show that the bi-directional gate recurrent unit proposed in this method has an average increase of about 0.1 in the R~2 and significantly improved the prediction accuracy compared with the comparison algorithm. It plays a certain role in decision support for the regulation of base station traffic and has a particular practical significance.更多还原